Using Vector Embeddings For Sentiment Analysis
Rod Acosta, Kevin Furbish, Ibrahim Khan, Anthony Washington
Introduction
- Sentiment Analysis:
- Defined as a branch of Natural Language Processing (NLP) that focuses on the computational treatment of opinions, sentiments, and subjectivity in digital text (Medhat, Hassan, and Korashy 2014).
Sentiment Analysis
- Importance of Sentiment Analysis:
- Essential for understanding public opinion, aiding businesses in refining marketing strategies, improving products, and enhancing customer satisfaction.
- The rise of social media and e-commerce platforms has increased the importance of sentiment analysis for real-time consumer behavior insights.
- Case Study: Twitter Data:
- Hasan, Maliha, and Arifuzzaman (2019) used Twitter data to demonstrate the potential of sentiment analysis.
- Their methodology combined Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) models with logistic regression, achieving an accuracy of 85.25% in classifying tweets as positive or negative (Hasan, Maliha, and Arifuzzaman 2019).
Sentiment Analysis (Continued)
- Kathuria, Sethi, and Negi (2022) applied sentiment analysis to e-commerce reviews using various machine learning models (logistic regression, AdaBoost, SVM, Naive Bayes, and random forest).
- Analyzed the Women’s E-commerce Clothing Reviews dataset to understand consumer behavior and the impact of electronic word-of-mouth (eWOM) on customer attitudes and product sales (Kathuria, Sethi, and Negi 2022).
- Nasukawa and Yi (2003) focused on extracting sentiments linked to specific subjects rather than entire documents.
- Their prototype system used a syntactic parser and sentiment lexicon to detect sentiments in web pages and news articles, offering detailed insights into specific opinions (Nasukawa and Yi 2003).
- Conclusion:
- These studies illustrate the evolution and application of sentiment analysis using NLP, highlighting its critical role in extracting insights from vast amounts of text data for strategic decision-making.
Vector Embeddings In Natural Language Processing
- Introduction to Embeddings:
- Vector embeddings are crucial for encoding or describing the sentiment of words or groups of words.
- Embeddings improve upon representing words as indices in a vocabulary by encoding relationships or similarities between words (Camacho-Collados and Pilehvar 2020).
- Advantages of Embeddings:
- Unlike simple vocabulary indices, embeddings can represent multiple meanings of words and their semantic and syntactic patterns (Pennington, Socher, and Manning 2014).
- Example: In the Word2Vec model, vector arithmetic can evaluate analogies (e.g., “king” - “man” + “woman” = “queen”)(Mikolov et al. 2013).
- Advanced Embedding Models:
- Sentence embeddings build on word embeddings to represent sentences, improving over Bag of Words models, which have high dimensionality and sparseness issues(Pilehvar and Camacho-Collados 2020).
- Document embeddings further extend this concept to encode entire documents for NLP tasks where word ordering is important(Pilehvar and Camacho-Collados 2020).
Using Embeddings For Sentiment Analysis
- Performance Improvement:
- Word embedding and deep learning models have significantly enhanced NLP tasks, including sentiment analysis (Kasri et al. 2022).
- Popular methods include Word2Vec and Global Vectors (GloVe), trained on different datasets (Google News and Wikipedia 2014, respectively) (N et al. 2024).
- Challenges with Pre-trained Embeddings:
- Pre-trained word embeddings often fail to capture contextual sentiment information, leading to inaccuracies (e.g., mapping “good” and “bad” to neighboring vectors) [Liang et al. (2023)](Tang et al. 2016).
- The paper aims to explore these challenges and investigate alternative embedding methods for more accurate sentiment analysis.
Methods
What is a neural Network?
- A neural network is a type of algorithm that mimics the structure and function of the human brain. Their goal is to create an artificial system that can process and analyze data in a similar way.
- There are different types of neural networks but there are some common elements between most of them. Those elements are:
- Artificial Neurons
- Layers
Neural Network Layers
- Neural networks usually have three types of layers:
- Input Layer
- Hidden layers
- Output layer
What are embeddings?
- Embeddings are a technique that allow us to map words or phrases into a corresponding vector of real numbers, where the position and direction of the vector capture the word’s semantic meaning in relation to other words.
- They make high-dimensional data like words readable to our algorithm/model and allows our model to recognize and learn meaningful relationships and similarities between words
Dense Layer & Cosine Similarity
- Cosine Similarity
- Measures the cosine of the angle between two non-zero vectors, providing a measure of similarity.
- The smaller the angle the higher the similarity between the two vectors.
- \(cosine\_similarity(u,v) = \frac{u.v}{||u|| ||v||}\)
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- Dense Layer
- A logistic regression model with a sigmoid activation function used for binary classification.
- It outputs the probability that the input belongs to a positive class.
- \(y=\sigma(W⋅z+b)\)
- Where:
- z is the flattened input vector.
- W is the weight vector.
- b is the bias term.
- \(\sigma(x) = \frac{1}{1+e^{-x}}\) is the sigmoid function.
Sentiment Analysis
- Through the use of a neural network and it’s hidden layers (embedding & dense), and the cosine similarity we are able to take inputs and classify them as being part of a positive or negative class based on what our model has learned from our training dataset.
Analysis and Results
Dataset Description
- Data set of 25,000 movie reviews from IMDB
- Max 30 reviews for each movie since popular movies are rated more often than unpopular movies.
- Includes only the top 5,000 most frequent words, minus the top 50 most frequent words
- IMDB reviews 1-10 star rating converted to a 0-1 scale
- Reviews already in vectorized format
Vectorization
- Neural Networks require numeric inputs, not the natural language of reviews
- Vectorization represents each word with a unique numeric substitution. For example the following texts:
- “this is fun”, “fun times ahead”, “fun is ahead of times”
- Results in a vocabulary of [this, is, fun, times, ahead, of]
- Vectorized observations: [1, 2, 3], [3, 4, 5] and [3, 2, 5, 6, 4]
- TensorFlow includes the IMDB dataset in a vectorized format
Statistical Modeling
Neural Network Implementation
- Neural network model implemented and trained using TF’s Keras
- TF and Keras implemented in Python and accessed from R via Reticulate
- Model learns embeddings for each word in the vocabulary
- Multi-dimensional embedding vectors placed close in the learned vector-space to vectors of similar words
- Similar means having a similar contextual meaning in the training dataset and its sentiment classification
- “gem” and “favorite” would be similar in context of a movie review, but not in a general context
Neural Network Implementation
- Keras Sequential model (model is built layer-by-layer)
- The neural networks leverages the Keras embedding layer
- Converts input vocabulary index into a vector of a chosen dimension
- Dimensionality important hyperparameter that controls compression vs overfitting
- Output of embedding layer is the dimensionality hyperparameter
- Embedding layer connects to a dense layer
- Dense layers require a 1D input vector so embedding layer output is flattened to 1D
- Dense layer has a 1 output unit with sigmoid activation function that receives the flattened embedding output
- Output unit is the sentiment score
- Back-propagation trains embedding layer weights to be similar based on sentiment
Determining Vector Dimensionality
- Number of dimensions for the embeddings must be selected
- Dimensionality selection often done ad hoc, or with grid search
- 2 through 7 dimensions were tested and compared by testing accuracy and qualitatively
Model Performance: 2D
- 2 dimensions had best accuracy, but failed to capture meaning
- Closest embeddings to the embedding for “awful”:
| word | cosine similarity |
|---|---|
| awful | 1.0 |
| lame | 1.0 |
| alcoholic | 1.0 |
| sadly | 0.99 |
| relevant | 0.99 |
| are | 0.99 |
Model Performance: 2D vs 7D
- 7 Dimensions had similar accuracy to 2, but better embedding performance
| reference word | closest words |
|---|---|
| awful | lame, alcoholic, sadly, relevant, are |
| mediocre | effort, turkey, terrible, stereotype, repeat |
| perfect | lovers, sing, manager, bath, donald |
| favorite | deeply, roud, marie, polanski, poetry |
| reference word | closest words |
|---|---|
| awful | ultimately, painful, sorry, fake, nowhere |
| mediocre | teeth, incompetent, main, disappointing, generous |
| perfect | great, seeking, freedom, tremendous, excellent |
| favorite | paulie, excellent, necessary, great, seeking |
Data and Visualization
Conclusion
- Optimal Embedding Dimensions: The best performance was achieved with 7 embedding dimensions, reaching an accuracy of 87.34% on the test dataset.
- Robust Performance: The model demonstrated competitive and consistent accuracy even with higher dimensions and minimal data preprocessing.
- Limited Vocabulary and Input: The model was trained on the top 5000 most common words and limited input sequences to the first 500 words, yet remained effective.
- Semantic Relationships: The model successfully captured semantic relationships, shown by coherent similar words for terms like “awful,” “mediocre,” “perfect,” and “favorite.”
- Avoiding Overfitting: Training was limited to 10 epochs to prevent overfitting, maintaining the model’s accuracy and reliability for practical sentiment analysis applications.
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